Core Technical Methods
Technical Architecture
RepoMind-AI's technical architecture consists of four parts: data ingestion layer, index construction layer, retrieval layer, and generation layer:
- Data Ingestion Layer: Retrieves source code, documents, and other information from GitHub, parses and preprocesses to extract key information;
- Index Construction Layer: Uses code embedding models to convert data into vectors and build indexes;
- Retrieval Layer: Supports dense, sparse, and hybrid retrieval, combined with metadata filtering;
- Generation Layer: Multi-model reasoning architecture that selects the appropriate model based on the task.
Application of RAG Technology
RAG solves the problems of insufficient domain knowledge and hallucinations in large models by introducing external knowledge bases. In code analysis, it can retrieve code information in real time, incrementally update indexes, and provide answer traceability.
Vector Embedding Technology
Uses code embedding models such as CodeBERT and GraphCodeBERT to capture semantic information, and vector databases like FAISS to achieve efficient similarity search.
Multi-Model Reasoning Strategy
Integrates specialized models for code understanding, architecture analysis, document generation, etc., and selects the appropriate model based on the problem type through an intelligent routing module.
